Abstract: In real-world，incomplete spatial data often have a heavy effect on the spatial decision，analysis and reasoning.So far，many statistical methods have been developed to deal with such incomplete items.However，these methods are not able to detect the incomplete spatial data since they ignore the spatial characteristics，e.g.spatial dependency among spatial data.This paper proposes a k-Nearest-Neithbors（k-NN） based approach for detection of incomplete spatial data，where the 3σ edit rule is used to identify the incomplete spatial data in the k-NN of each of spatial samples.Finally，a practical example is used to illustrate the rationality of the proposed approach in this paper.

摘要： 不完备的空间数据影响了空间决策、分析与推理的结果及其可靠性。传统的不完备数据检测方法仅使用统计学理论，没有考虑空间数据的空间特性，从而不能直接用于检测不完备的空间数据。提出了一种基于邻近域的不完备空间数据检测方法—NNBiSDD算法，NNBiSDD算法在空间实体的k-邻近域内使用“三倍标准差”原则检测不完备的空间数据。最后，通过一个实际算例验证了NNBiSDD算法的有效性和可靠性。